Technology

At the heart of our breakthrough platform is the Large Essence Model (LEM)

Just as Large Language Models (LLMs) like GPT understand human text, the Large Essence Model (LEM) is trained to understand scent. Using a transformer-based AI architecture and federated learning, LEM translates the complex signatures of volatile compounds into meaningful insights.

How LEM Works:

• Molecular to Meaningful: This process turns raw molecular signals into actionable classifications, such as identifying spoiled food, airborne toxins, or a specific wine varietal.

• Adaptive & Private: Learns from millions of real-world data points without ever storing personal information, using privacy-preserving federated learning.

• Always Evolving: LEM constantly adapts to new compounds and environmental changes via decentralized, verified updates.

• Distributed Inference: LEM runs across a global network of registered nodes, delivering low-latency, high-accuracy scent classification at the edge or in the cloud.

A closed feedback loop

1. Devices capture volatile compound data from the physical world.

2. LEM trains on this validated data, improving its scent classification abilities.

3. New insights flow back to users and devices via distributed inference.

This architecture transforms scent from a forgotten sense into a continuously evolving layer of machine-readable meaning, unlocking vast potential for safety, health, commerce, and creativity.